Online detection of internal defects in products or produce, e.g. food items, using X-rays is known in the art for detecting defects that are easily discernable on radiographs. Particularly, X-ray imaging has become a valuable tool in many industrial branches to perform non-destructive tests for ensuring the quality of a product. Since most materials are translucent to X-rays, internal defects can be visualized without cutting open and damaging the product. For example, the use of two-dimensional X-ray radiographic imaging for non-destructively testing of the quality of products and/or detecting defects in products is known in the art, e.g. for inline inspection of food products in the food industry. Such a simple 2D radiographic projection, in which all features on the inside and the outside of the object are superimposed into one single image, may provide a fast way to visualize the interior of an object inline.
X-ray systems are commercially used for foreign body detection and, yet more limited, for the detection of certain common internal defects and unwanted properties in food systems such as insect presence in fruit, e.g. as disclosed by Haff et al in “X-ray detection of defects and contaminants in the food industry,” Sensing and Instrumentation for Food Quality and Safety, 2(4), pp. 262-273, and by Jiang et al. in “An adaptive image segmentation algorithm for X-ray quarantine inspection of selected fruits,” Computers and Electronics in Agriculture, 60(2), pp. 190-200. It is also known in the art to use X-ray imaging for automatic fish bone detection, e.g. as disclosed by Mery et al. in “Automated fish bone detection using X-ray imaging,” Journal of Food Engineering, 105(3), pp. 485-492.
This approach, as known in the art, may however have several disadvantages. For example, density differences need to be large enough for defects and/or unwanted properties or objects to be visible in projection radiographs. This implies that this approach may not be useable in particular applications. Furthermore, custom algorithms may need to be developed for every type of defect or unwanted property that should be detected. This can prove to be very time consuming, certainly when taking into account that when imaged in different hardware setups, appearance of these defects can differ substantially.
To detect subtle features, a full three-dimensional CT reconstruction of the object may be needed, since particular internal defects cannot be discerned on projection images captured from a single point of view, or even by simultaneously evaluating a plurality of images corresponding to a plurality of complementary projection views, e.g. images corresponding to two or more projection views along mutually orthogonal projection axes. For example, in the food industry, some defects, such as browning disorders in fruit, inherently show low contrast with respect to their surroundings and can be very small.
Classical CT imaging methods imply that projections are taken from many angular positions around the sample, either by rotating the source-detector pair, e.g. in an arrangement commonly used for medical scanners, or by rotating the object sample while imaging the object, as may be known for industrial setups. This approach may have several implications when applied in online inspection system for inspecting an object conveyed by an inline transport system. For example, rotating the source-detector pair around a conveyor belt is impractical because of the high speeds that would be required to maintain an acceptable throughput speed of the conveyor belt. A high speed rotating gantry would require very expensive hardware, cause massive forces, imply additional safety constraints and make the hardware large and bulky. Furthermore, rotating the object sample over a large enough angular range for CT imaging, while moving on a conveyor belt, may also be undesirable because the rotation would also require a high speed and precise control, which is practically difficult to achieve. Even if these problems could be circumvented, an image processing algorithm may need to be developed for every type of defect or unwanted property that should be detected.
Due to cost, time and hardware constraints, a full 3D tomographic reconstruction is therefore difficult to achieve, or even infeasible, in an in-line application, e.g. in an inline sorting system for sorting x-ray transparent objects that are moving in an object stream, e.g. products or produce, such as a vegetable or a fruit, moving on a conveyor belt or similar conveying system. Moreover, the complexity of 3D CT imaging techniques as known in the art can have the disadvantages of being costly and complex and may substantially compromise the desired production line throughput when providing a sufficient image quality to ensure an acceptable defect detectability. For example, the trade-off between a high acquisition speed and a high contrast and resolution image, may be one of the main reasons why 3D X-Ray CT has not yet touched ground as an inspection tool in food industry. In other industrial branches, it may however be known to use CT in-line or at-line, for example as a metrology tool, for example using high throughput batch delivery systems or a continuous throughput conveyor belt system using a helical scanning approach. Nevertheless, such approaches remain quite costly and complex.
Attempts have been made to circumvent the problems mentioned above. For example, Rapiscan Systems developed an online CT-scanner for baggage inspection by combining a large number of source-detector pairs into one setup, e.g. the Rapiscan RTT™ 110 of Rapiscan Systems, Torrance, Calif. 90503 USA. This functioning yet expensive solution may reach speeds of 1500-1800 bags per hour, corresponding to a throughput speed of about 0.5 m/s, which may not be fast enough for a high volume, low value application such as the food industry.
In “Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces,” Computers and Electronics in Agriculture, 96 (2013), pp. 238-245, by Johansson et al., a method was disclosed that combines three-dimensional scanning and X-ray radiographs. However, a disadvantage of this method is that the data processing proposed in this prior art article is limited to the estimation of heartwood diameter and density in logs.
Another approach is to use the translation of an object on the conveyor belt to get projections in a limited angular range. However, a three-dimensional reconstruction from projection data in a limited angular range is not straightforward and may introduce large image artefacts. Research into this subject has been reported, e.g. by Iovea et al. in “Pure Translational Tomography—a Non-Rotational Approach for Tomographic Reconstruction,” Proceedings of the 9th European Conference on NDT ECNDT, Tu.1.4.1.